Method for dominant color setting of video region and data structure and method of confidence measure extraction

ABSTRACT

A method for a dominant color setting of a video region and a data structure and a method of a confidence measure extraction are disclosed. The video region dominant color setting method is characterized in that a region dominant color descriptor is expressed by the number of dominant colors with respect to a certain region, a dominant color expressed, a frequency that the dominant color appears, and an accuracy of a color value representing the region in a region dominant color based on various region dominant color extraction methods, for thereby expressing a region dominant color using a plurality of colors with respect to a region dominant color value and a confidence value of a region dominant color information based on various region dominant color feature extracting methods.

This application is a Continuation of prior application Ser. No.11/133,212, filed on May 20, 2005, now U.S. Pat. No. 7,760,935, which isa Continuation of prior application Ser. No. 09/609,392, filed on Jul.3, 2000, now U.S. Pat. No. 7,417,640, which claims priority to KoreanApplication No. 26784/1999 filed in Korea on Jul. 3, 1999, and which isa Continuation-In-Part of prior application Ser. No. 09/239,527, filedJan. 29, 1999, now U.S. Pat. No. 6,445,818, The above mentioneddocuments are incorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a dominant color feature descriptionused in a content-based multimedia data retrieval system, and inparticular to a method for setting-up a video region dominant color adata structure therefor, and a method for extracting a confidencemeasure, which are capable of expressing an object and a color of aROI(Region Of Interest) in a video during a multimedia indexingoperation.

2. Description of the Background Art

In a multi-media search system, there are various methods for expressinga color feature of an object and a ROI of a video in a multi-mediasearch system. The above-described methods are applied differently inaccordance with each system.

There are various methods for expressing a dominant color, such as amethod for using an average color value of a region, a method forexpressing the most frequently appearing color, a method for expressingn-number of the most frequently appearing colors, a method for using acolor appearing in a region predetermined by threshold of P % or above,and a method using a color histogram.

Each of the above-described conventional methods has its own advantagesand disadvantages. For example, the method of using the histogram has anadvantage to describe color information in detail. However, it also hassome problems in that the histogram has relatively large size of dataand some colors represented by corresponding histogram bins can beconsidered as they have unnecessary region dominant color values withrespect to those colors occupying relatively small regions.

In the case that a region dominant color value is expressed by oneaverage value, there are advantages in that it is a compressed datadescriptor and used for pre-filtering in a content-based searching.However, in the case that the region colors are formed in variouscolors, it is impossible to express the dominant color featureaccurately.

Recently, a data structure for extracting the region dominant color isbeing standardized. However, if a unique method for the extraction ofthe region dominant color is not standardized and only data structure isstandardized, it is impossible to maintain a compatibility of the databuilt in each system where a plurality of systems are used.

In addition, even when extracting the dominant color values by the samemethod, it is hard to achieve reliable performance in every case.

For example, beside the problems presented when the average color isused as a dominant color, when the histogram is used to express thedominant color feature, the performance depends on the number ofhistogram bins, namely, the number of color levels.

If there are too large number of bins, the region color is unnecessarilyexpressed by too many colors for thereby decreasing a searchperformance, and when the region color is expressed by too few colorswith a small number of bins, the region formed of various colors is notproperly expressed, so that the search performance is degraded.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide amethod for setting-up a dominant color of a video region which iscapable of expressing a region dominant color using a plurality ofcolors with respect to a region dominant color value and a confidencevalue of a region dominant color information based on various regiondominant color feature extraction methods.

It is another object of a present invention to provide a data structurefor the dominant color setting of a video region.

It is still another object of the present invention is to provide amethod for extracting a confidence measure wherein the dominant colorsetting of a video region according to the present invention.

To achieve the above objects, a video region dominant color descriptoris provided to characterize the number of dominant colors, dominantcolors, the frequency per dominant color respectively with respect to acertain region, and the confidence measure of the dominant color valuesand the frequencies extracted based on various region dominant colorextraction methods.

Additional advantages, objects and features of the present inventionwill become more apparent from the description which follows.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become better understood from the and theaccompanying drawings which are given by way of illustration only, andthus are not limitative of the present invention, and wherein:

FIG. 1 is a flow chart illustrating a region dominant color settingmethod according to the present invention;

FIG. 2 is a flow chart illustrating a descriptor search method using aregion dominant color settlement according to the present invention; and

FIG. 3 is a block diagram illustrating an interoperability maintainingmethod between different systems using region dominant color extractiondescription data according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

According to one feature of the present invention, the expression methodof the region dominant color extraction method is formulated based on anextraction method type, a pre-processing description, a frequencycondition description, color space description, a color sub-spacedescription, a quantization description, a color clustering description,etc. for thereby maintaining an interoperability between differentsystems.

According to another feature of the present invention, the similaritybetween a dominant color and the similar color with the dominant color,a coherency of the color with respect to a color given, a differencebetween the dominant color value and the accurate value of the colorwhen the color is considered as a certain color, a size of the regionwhich covers the dominant color in an image region, and the positions ofeach color pixel in the region are adopted in order to calculate theconfidence measure, so that it is possible to compare the regiondominant colors values based on different feature extractions.

In addition, by expressing a confidence value for the entire regiondominant colors and/or each color, it is possible to obtain a descriptorthat describes the more accurate region dominant colors.

As shown in FIG. 1, the video region dominant color setting methodaccording to the present invention includes a step for extracting aregion R from a visual data(video and/or images), a step for setting adominant color descriptor(DCD) with respect to the region provided, anda step for storing a region descriptor with respect to the regiondominant color descriptor and the region information.

The DCD is described by the number N of the dominant colors of the colordescriptor with respect to the region given, a certain dominant color Cidescribed by a color information (e.g. r, g, b components, etc.) and afrequency Pi which describes the degree that the dominant color appears,and a CM(Confidence Measure) of the color descriptor value.

As shown in FIG. 2, the descriptor search method using a region dominantcolor includes a step for selecting a region by a user and extracting aregion descriptor corresponding thereto, and a step for extracting adominant color descriptor value with respect to a corresponding regionand comparing the extracted dominant color descriptor value with each ofall stored region dominant color descriptors.

As shown in FIG. 3, in the interoperability maintaining method betweendifferent systems using formalized data for a region dominant colorextraction method, a region dominant color descriptor(DCD) with respectto a region descriptor R of each system A and B is obtained, and theregion dominant color extraction method is formalized, and the regiondominant color descriptor and the description of region dominant colorextraction method are converted into a sharing data format, and then acomparison search is performed with respect thereto.

The formalized data structure for description of the region dominantcolor extraction method includes an extraction method type forextracting a region dominant color, a pre-processing description fordescribing a filtering method of a certain region when obtaining theregion dominant color value, a frequency condition description type fordescribing a condition of a frequency of a dominant color which isobtained by a histogram, a color space description type for describing adescriptor with respect to a color space used for describing the regiondominant color, a color sub-space description for defining whether theregion dominant color is expressed in a sub-space of the definedreference color space, a quantization description for describing aquantization method of the color space, and a color clusteringdescription for describing when the region color is expressed based onthe color clustering method.

In addition, the extraction method description includes a method usingan average color value of a certain region, a method for expressing onemost frequently appearing color, a method for expressing N number ofmost frequently appearing colors, a method for using colors whichappears more than P % of threshold value in the predetermined region ora method for using a color histogram.

In the pre-processing description, it defines a filter type establishedwhen a is region dominant color value is obtained, a filter size adaptedin the image region, and a filter sliding method of a filter window.

The frequency condition description defines a frequency threshold fordefining in a threshold value of a frequency above which the colors areset to the region dominant color, a sorting order description fordesignating the number of n top frequency colors of a region dominantcolor, and a frequency sum of top n frequency thresholds of thefrequently appearing threshold value of the higher n frequency.

The color space description defines reference color space which is areference of a dominant color and a transformation description from areference color space to define the transformation from a well knowncolor space to the adopted color space, wherein the transformationdescription defines the number of color channels of the reference colorspace(?) and a transformation type and method.

In the color sub-space description, it defines the number of colorchannels and the color channels used, and a range of the channel, and avector sub-space type with a method for the type, when the regiondominant color only considers a sub-space of a color space.

In the quantization method description, in order to describe thequantization method of the color space, the quantization descriptiondefines the number of quantized channels and the quantized colorchannels, the quantization method and the number of the quantizationlevels for each channel, and a method used for a quantizationtransformation.

In addition, the color clustering description defines whether theclustering is used or not, and whether or not the number of clusteringis varied in accordance with the region, the number of the clusters andthe color channels used in the clustering and the method to describeeach cluster.

Therefore, it is possible to perform a search among data constructedfrom different DOD extraction methods in different systems using the DCDextraction method description, and a search by unifying two DCDextraction methods into one method.

In addition, by adopting the DCD extraction method description, theconfidence measure can be obtained for expressing the degree of accuracyof the region dominant color for thereby enhancing a search performanceand implementing a compatibility among the region dominant colors whichare extracted by different extraction methods.

The confidence measure is determined by all or part of factors such as aNADCA(Not Apparently Distinguishable Color Allowance) which is a maximumvariance that any two colors are recognizable as the same color, and acoherency value for measuring whether or not the pixels of the colorsare gathered with respect to the color given, and a CME(Color MappingError) which is related to an error between all color values mapping tothe dominant color and the dominant color value i.e. CME is the propertyof the color variance of the colors clustering a dominant color, and thesize of the region covered by the dominant color in the image region,and the position of the color pixels in the region.

The confidence measure extraction method includes a step forinitializing the confidence measure and the count sum of the pixels, astep for obtaining a coherency value and the counting value of thecorresponding color pixels with respect to all dominant colors Ci andadding a confidence of the initial value to the value obtained bymultiplying the coherence value and the counting value of the colorpixels for thereby obtaining a confidence with respect to all colors,and a step for obtaining a confidence with respect to the image regionby dividing the obtained confidence value into the region size.

In addition, a confidence is obtained with respect to each color using aconfidence extraction method.

The video region dominant color setting method will be explained withreference to the accompanying drawings.

The DCD(Dominant Color Descriptor) capable of expressing the colors ofan object appearing in a visual data(video and/or images) or a region ofinterest (ROI) during a multimedia indexing operation is set.

The region dominant color descriptor is a color descriptor with respectto a certain region and is determined based on the entire images or apart of the image of the region, a video segment, a region having anirregular shape based on the time variance with respect to an objectlike a video segment, and a region for expressing a regular position inaccordance with the time of the video segment.

The DCD is expressed based on the number N of the dominant colors withrespect to a region provided, an I-th dominant color Ci, a frequency Piof the dominant color Ci, and a CM(Confidence Measure) expressed by anaccurate color value which represents the region.

Namely, DCD: [N, {Ci,Pi)|0<i≦N}, CM]

where N represents the number of the dominant colors in the DCD, Cirepresents an i-th expressed dominant color (0<1≦N) in the DCD, Pirepresents a frequency (0<i≦N) that the dominant color Ci appears in theregion, and CM represents a confidence, namely, the accuracy of thecolor value and/or percentage value which represents the region.

Here, the dominant color Ci is defined by a plurality of parameters.Namely, it is formed of a color space description, a quantizationdescription, a color clustering is description, and a channeldescription such as the number of color channels.)

Therefore, it is possible to express the region dominant color based onan expression method of the DCD with respect to the region dominantcolor value in accordance with various region dominant color featureextraction methods, namely, a plurality of colors, and the confidence CMof the color.

For example, when the DCD1 is expressed by DCD1=[N=1, {C0=(r,g,b),P0=UNDEFINED)}, CM=k] based on the average color method, the number N ofthe dominant colors is 1, and the expressed dominant color(C0) becomesan average color (r,g,b) of the region, and the frequency P0 isexpressed as UNDEFINED, and the confidence CM is a confidence value k inwhich the average value represents the region.

In addition, in the histogram, in the case that the DCD5 is expressed asDCD5=[N=64, {(C0=(r1,g1,b1), P0=10%), (C1=(r2,g2,b2), P1=5%, . . . ,(C63=(r63,g63,b63), P63=1%)}, CM=0.99], the number N of the dominantcolors is the number of the histogram bin. Therefore, when expressingthe histogram using 64 bins, N equals 64 and C0˜C63 are expressed by thecolor values of a corresponding bin.

If the number of quantization levels is too large or too small whenforming the histogram, the confidence has a small value. Accordingly, itis possible to check whether a proper number of quantization levels areobtained based on the confidence CM value.

FIG. 2 illustrates a description search method which is implementedusing the region dominant color. In this method, if user select aregion, a region descriptor corresponding thereto is extracted, and thedominant color description with respect to the above-describedcorresponding region is extracted. All stored region dominant colordescriptors and the extracted dominant color descriptor are compared.

Therefore, since all region dominant color descriptors and the extracteddominant color descriptor are compared, it is possible to perform adescriptor search using the dominant color descriptor with respect toall region descriptors.

In addition, FIG. 3 illustrates a method for maintaining aninteroperability between different systems using a region dominant colorextraction method description.

In this method, a region DCD with respect to the given region R of eachsystem A and B is extracted, and feature extraction method of the regiondominant colors is described.

By transforming the above-described DOD into a sharing data format, acomparison search can be performed between different systems. Inaddition, by transforming each of the formalized data of the region DCDinto a sharing data format and performing a comparison search betweenthe different systems, an interoperability between the different systemscan be maintained.

The description with respect to the extracting method of the regiondominant color uses the following items (item 1 through item 7) todescribe different extracting method of each region dominant color. Eachitem is divided into small items.

In the extraction method type of the item 1, it defines a method usingan average color of the region, a method using one color which is mostfrequently appeared, a method for expressing an n number of mostfrequently appearing colors, a method using a color which appears morethan P % of threshold value in the predetermined region or an extractionmethod using a histogram.

In the pre-processing description of the item 2, it defines a format ofpre-processing for smoothing and burring a region when obtaining adominant value of the region. Such a pre-processing description includesa filter type (for example, an average filter, etc.), a filter size (forexample, n,m/whole/, etc), and a filter sliding method (for example,1,1/2,3/non-over lap, etc.) for representing how to slide the filterwindow when adapting the filter.

The frequency condition description of item 3 is directed to how to usethe frequency in which the dominant colors appear by obtaining thehistogram.

In detail, it defines the threshold value of the frequency in which thefrequency below the threshold value is not considered, the sorting orderthreshold value in which the dominant colors are set with respect to afew number among the frequencies which appear n most frequently, and thesum of the frequencies as threshold value, which appear n mostfrequently.

The color space description of item 4 is directed to a descriptor withrespect to the color space itself used for indicating the regiondominant color.

In detail, the reference color space (for example, RGB, HSV, etc.) isdefined, and a transformation relationship between the reference colorspace and a certain well known color space is described.

Namely, in the transformation description from the reference colorspace, the number of color channels of the adopted color space and thetype of transformation (linear type/non-linear type) from the referencecolor space to the adopted color space are defined, and thetransformation is defined.

In the case that the transformation method to the color space is alinear type, a transformation matrix is defined, otherwise (in the caseof the non-linear type), the C-code type is used for a definition method(for example, a definition based on an equation and a certaincondition).

The color sub-space description of item 5 is directed to recognizewhether the region dominant color is expressed in a certain sub-space ofthe color space defined by the color space.

In detail, in the case that the sub-space is considered, the number ofthe color channels and a corresponding color channel are defined, and itis defined whether the type of the vector sub-space is adopted or not(vector space type/non-vector space type), and the range of each channelis provided.

Here, since the channel range is expressed by a variable, and thechannel range is changed dependent of the condition of the item.

If the vector sub-space type item is a non-vector space type, the methodis not defined, and otherwise the method is clearly defined. At thistime, the re-definition is clearly performed whenever the condition ischanged.

The quantization description of item 6 is directed to a quantizationmethod of the color space.

In detail, the number of the quantized channels, the quantized colorchannels, and the quantization type (linear type/non-linear type/vectorquantization type) are defined. In addition, the number of quantizationlevels of each channel and the thusly defined quantization type aredefined in detail.

If the quantization type is a linear type, it is described, and if thequantization type is a non-linear type, one vector is described for onecolor channel. In addition, in the case of the vector quantization type,it is defined by an equation and a condition method. In addition, it ispossible to clearly express using a look-up table.

The color clustering description of item 7 is directed to expressingwhether the color is clustered to be color quantization.

If the level type is not fixed, it is expressed that the number ofclustering is varied in accordance with the region, and the number ofthe clusters and the clustered color channels are expressed for therebydefining each cluster.

When defining each cluster color, it is expressed based on a parameterof an ellipsoid and a centeroid of the ellipsoid.

For an example of the extraction method description for the extractionmethod using an average color among various extraction methods of eachregion dominant color, it will be explained as follows.

1. Extraction method type=average color

2. Preprocessing description:

-   -   2-1. Filter type=Average filter    -   2-2. Filter size=whole    -   2-3. Filter sliding method=non-overlap

3. Frequency condition description:

-   -   3-1. Frequency threshold=0% or n/a    -   3-2. Sorting order threshold=n/a    -   3-3. Frequency sum of top n frequencies threshold=100% or n/a

4. Color space description:

-   -   4-1. Reference color space=RGB    -   4-2. Transformation from reference color space description:        -   4-2-1. Number of color channels=n/a        -   4-2-2. Uniform type transformation=n/a        -   4-2-3. Method definition=n/a

5. Color sub-space description:

-   -   5-1. Sub-space used=FALSE    -   5-2. Number of using color channels=n/a    -   5-3. Using color channels=n/a    -   5-4. Channel ranges=n/a    -   5-5. Vector sub-space type=n/a    -   5-6. Method definition=n/a

6. Quantization description:

-   -   6-1. Number of quantized channels=3    -   6-2. Quantized color channels={channel 1, channel 2, channel 3}    -   6-3. Type=uniform type    -   6-4. Number of quantization levels per channel=(4,4,4)    -   6-5. Quantization definition=n/a

7. Color clustering description:

-   -   7-1. Clustering used=FALSE    -   7-2. Fixed level type=n/a    -   7-3. Number of clusters=n/a    -   7-4. Clustered color channels=n/a    -   7-5. Cluster definition=n/a

Namely, in the description of the extraction method using an averagevalue, the type of the extraction method of item 1 is directed toextracting an average color.

Item 2 is directed to a pre-processing description. In the filter type2-1 in the detailed item, an image region is average-filtered by anaverage filter, and what the filter size 2-2 is “whole” represents thatthe entire values are averaged not average-filtering the image regionusing a certain filter size. In addition, what the filter sliding method2-3 is “non-overlap” represents that the earlier filter window is notoverlapped with the later filter window when adapting the filter window.

In item 3, when obtaining the histogram, and the frequency is used,since the threshold value 3-1 is 0% or n/a, it means that theabove-described value is not considered. In addition, since the sortingorder threshold value 3-2 is n/a, it means that the above-describedvalue is not considered. The threshold value 3-3 represents that it isnot considered since the frequency sum of top n frequencies threshold is100% or n/a.

Item 4 is a descriptor with respect to the color space itself, and thereference color space 4-1 and the transformation description 4-2 fromthe reference color space are directed to expressing a transformationrelationship between adopted color space and the reference color space.

Namely, the reference color space is a RGB space, and since thetransformation description 4-2 is n/a, it means that there is no colorspace which is newly adopted, and the color space which expresses theregion dominant color value is RGB.

In the case that the RGB and other color space are used, and atransformation between the color space and the RGB is described, if thelinear type is TRUE, one transformation matrix is defined and expressed,and in the case that the linear transformation is not defined, theequation and/or conditional sentence is used for thereby defining theitem.

The description of the color sub-space of item 5 is directed to checkingwhether the region dominant color is expressed in a sub-space of acertain color space defined by item 4. Since the used sub-space is setas FALSE, the region dominant color value does not consider a certainsub-space.

The quantization description of item 6 is directed to a quantizationmethod of the color space. The number 6-1 of the quantized channels is3, and the three quantized color channels 6-2 are channel 1, channel 2and channel 3. In addition, since the number of the quantization types6-3 is 4,4,4, this means that the channel of each R,G,B is quantized by4,4,4, respectively so that the member of quantization levels is “64”.

In the case that the quantization type of 6-3 is a non-uniform, onevector must be described per one color channel to define quantizationpoint per channel, and in the case of the vector quantization type, itis expressed by some arithmetic expression.

In item 7, the color is not clustered in this example, therefore thisitem is not used (Clustering used=FALSE).

For another example of the extraction method description, thedescription with histogram extraction method is explained.

In the following extraction method, up to 10 colors are defined as theregion dominant colors which are the most frequently appearing top tencolors, and a histogram with respect to the region is obtained andcolors corresponding to the histogram bins are defined as the dominantcolors with the condition that the frequency below 1.5% is excluded.

The items are set as follows to express this extraction method.

<Extraction Method Description Using Histogram>

1. Extraction method type=At most top ten frequently appearing colors:

2. Preprocessing description:

-   -   2-1. Filter type=Average filter    -   2-2. Filter size=5,5 (means 5 by 5 filter)    -   2-3. Filter sliding method=1,1

3. Frequency condition description:

-   -   3-1. Frequency threshold=1.5%    -   3-2. Sorting order threshold=10    -   3-3. Frequency sum of top n frequencies threshold=n/a (or 100%)

4. Color space description:

-   -   4-1. Reference color space=RGB    -   4-2. Transformation from reference color space description:        -   4-2-1. Number of color channels=3        -   4-2-2. Uniform type transformation=FALSE        -   4-2-3. Method definition=input ranges: r=(0,255), g(0,255),            b=(0,255);        -   output ranges: C1=(0,255), C2=(0,255), C3=(0,360);        -   C1=max(r,g,b)        -   if max(r,g,b)=0, C2=0;

else,

${C\; 2} = \frac{{\max\left( {r,g,b} \right)} - {\min\left( {r,g,b} \right)}}{\max\left( {r,g,b} \right)}$

if max(r,g,b)=0, C3=UNDEFINED

else if r=max(r,g,b) & (g−b>0)

${C\; 3} = \frac{\left( {g - b} \right) \times 60}{{\max\left( {r,g,b} \right)} - {\min\left( {r,g,b} \right)}}$

else if r=max(r,g,b) & (g−b<0)

${C\; 3} = {360 + \frac{\left( {g - b} \right) \times 60}{{\max\left( {r,g,b} \right)} - {\min\left( {r,g,b} \right)}}}$

else if r=max,

${C\; 3} = {120 + \frac{\left( {g - b} \right) \times 60}{{\max\left( {r,g,b} \right)} - {\min\left( {r,g,b} \right)}}}$

else

${C\; 3} = {240 + \frac{{\left( {g - b} \right) \times 6} -}{{\max\left( {r,g,b} \right)} - {\min\left( {r,g,b} \right)}}}$

5. Color sub-space description:

-   -   5-1. Sub-space used=TRUE    -   5-2. Number of using color channels=1    -   5-3. Using color channels=C1    -   5-4. Channel ranges=0,360    -   5-5. Vector sub-space type=FALSE    -   5-6. Method definition=n/a

6. Quantization description:

-   -   6-1. Number of quantized channels=1    -   6-2. Quantized color channels=C1    -   6-3. Type=uniform type    -   6-4. Number of quantization levels per channel=24    -   6-5. Quantization definition=n/a

7. Color clustering description:

-   -   7-1. Clustering used=FALSE    -   7-2. Fixed level type=n/a    -   7-3. Number of clusters=n/a    -   7-4. Clustered color channels=n/a    -   7-5. Cluster definition=

In detail, item 1 describes “at most top 10 frequently appearing colors”as the extraction method type.

In the preprocessing description of item 2, 2-1 represents that theaverage filter of the region is adopted, and 2-2 represents that thesize of the filter having 5 by 5 is used, and what the filter slidingmethod of 2-3 is 1, 1 represents that the center of the filter is movedby 1, 1 in the filter window in vertical and horizontal directions.

Item 3 is for the frequency condition description. Since the frequencythreshold value of 3-1 is 1.5%, except for the frequency that is below1.5%, the sorting order threshold value of 3-2 is 10. Therefore, themaximum 10 colors are designated as the dominant colors according to thefrequency of the colors in the histogram, and n/a of 3-3 represents thatthis item is not considered.

Item 4 is a color space description. The reference color space is RGB,and the number of the color channel 4-2-1 of the color space for thetransformation description 4-2 of the reference color space is 3, andthe uniform type transformation is set to FALSE. therefore, thetransformation between the color space adopted and RGB is a non-uniformtransformation. In 4-2-3, the condition with respect to the non-uniformtransformation method is described.

In addition, in the condition 4-2-3 of the non-uniform transformationmethod, the input ranges and output ranges of each channel are defined,where the output ranges based on the input conditions are defined.

Item 5 is the description of the color sub-space and is directed tocheck whether the region dominant color is expressed in a sub-space ofthe color space defined in item 4.

Since the used sub-space is set to TRUE, it is known that the regiondominant is color value considers a certain sub-space, and in 5-2, 5-3,and 5-4, it is known that one color channel C1 is considered as achannel range value of 0˜360.

The quantization description of item 6 is directed to a quantizationmethod of the color space, and number(6-1) of quantized channels is 1,and the quantized channel 6-2 is C1, and the quantization type 6-3 isdefined as a uniform quantization type, and it is not needed to have amethod definition 6-5.

In addition, the number 6-4 of the quantization levels of each channelrepresents that the channel C1 is quantized to 24-levels.

In addition, item 7 is directed to checking whether the color isclustered or not. The use of the clustering is set to FALSE which meansthat the clustering is not used.

The above-described data structure are defined in the header part of thememory, and whenever each item is changed, the item is re-defined.

Therefore, it is possible to clearly describe the meaning of thedominant color description among the different feature extractionmethods based on the above-described feature extraction method, therebythe interoperability is satisfied in comparison search among datagenerated by different systems.

Namely, it is possible to conduct a comparison search by checking anextraction method with respect to the region dominant color descriptorusing an extraction method description and by a step(sharing data formattransformation) for integrating two region dominant color descriptors tobe compatible. In addition, it is possible to maintain aninteroperability between other feature extraction methods using asharing data with respect to the region dominant color extractionmethod.

The confidence measure CM of the region dominant color is a descriptorwhich represents an accuracy of the expressed region dominant color andrepresents whether a corresponding region is expressed by one color andso on. The confidence CM is set by numeral values which represent thedegree of confidence when the color property of the region is expressedby dominant colors.

The above-described confidence measure can be expressed by thenormalized values of 0˜1, and the confidence measure may be expressed bya vector value.

For example, CM=[C,ACME,P,AISI].

Here, C represents a normalized coherency (image spatial variance), andACME represents an average of color mapping error value, P represents avalued obtained by summing the frequencies of all region dominant colorvalues, and AISI represents an average of image space importance.

Therefore, when the confidence measure CM is expressed by a few colors,it is more useful. Namely, it is difficult to express the region by afew colors especially when the region consists of various colors. Atthis time, the value of the confidence is very important.

In addition, when the value of the confidence CM is low means that theregion is formed of complicated various colors. Therefore, it is usefulfor a searching operation. In the case that more than one extractionmethod are provided for each region, or another feature descriptor isprovided, various methods taking advantage of the confidence measure canbe used.

For example, in the case that the value of the confidence measure of theregion dominant color extracted by the average value extraction methodis low, it is possible to use other descriptors such as a regiondominant color descriptor based on the histogram extraction method, etc.

In addition, when the region dominant colors are expressed by aplurality of dominant color values based on a certain method such as anextraction method of an n-number of most frequently appearing colors, itis possible to check whether a proper number of regions is expressed ornot using the confidence measure value.

The elements which are selectively adopted for extracting theabove-described confidence measure will be explained.

First, when one color is expressed by a certain value, the color isvaried in accordance with an increase/decrease of the color value. Atthis time, the maximum variation value(NADCA:Not Apparently Distinguishcolor Allowance) which may be recognized as the similar color can exist.

Namely, it is not judged by whether people can distinguish the slightcolor difference by the maximum variation. Instead, it is judged bywhether colors within the maximum variation are recognized as thesimilar color by human, especially in a content-based image search.

A blue sky image is expressed by hundreds of colors, so that the imageis naturally seen by the human eye. In the content-based image search,it is possible to express one color, namely, a certain blue color, sothat too many color separations are not needed during the content-basedimage search.

In particular, when obtaining the region dominant color value based onan average value, it is possible to obtain the confidence measure valuebased on a frequency of the region that the average value covers theimage region by defining the NADCA value.

In addition, a coherency value(COH) is adopted to measure whether thepixels of the color are gathered or scattered with respect to a colorgiven. The coherency value has a value of 0 to 1. As the coherency valueis increased, the confidence value is increased.

When a certain color Pj is considered (mapped) as a dominant color Ci inthe image region, where respective Pj and Ci is expressed by one pointin the color space, there is an error (CME: Color Mapping Error) betweenthe accurate value and the dominant color value of the colors. As thedifference is decreased, the confidence is increased, and the differenceis increased, the confidence is decreased. This can be measured by colorvariance in the color space.

Namely, CME is as follows:

${CME} = \underset{FO}{Q}$

In addition, the size Pi of the region that the dominant color covers inthe image region is reflected to the confidence. As the size of theregion that the dominant color covers is increased, the confidence ofthe dominant color is increased.

The confidence is reflected based on an ISI(Image Space Importance) in aregion R of each color pixel. For example, if the color pixels arepositioned at the center portion of the image, the colors may beconsidered as a more important color, and if the colors are positionedat an edge portion of the region, the colors may be considered as a lessimportant color. Therefore, the reliability is increased when the colorsof the image region which are expressed based on the representativecolor value are positioned at the center portion.

Namely, when the extracted confidence is high represents that thedominant colors are distanced from each other within the region, and inthe case that the quantization step is near an actual NADCA value, theregion colors cover the entire regions.

In addition, when the confidence is low represents that dominant colorsare mixed, or the quantization steps are actually far from the NADCAvalue. At this time, the region colors do not fully cover the region.

The algorithm for extracting the reliability is performed by thefollowing steps:

a) A step for setting the confidence to an initial value(=0) isperformed;

b) A step for setting the sum(SUM_COUNT_PELS) of count pixels is set toan initial value(=0) is performed;

c) A value(COUNT PELS_Ci) obtained by counting the color pixelscorresponding to each region dominant color with respect to all regiondominant colors and a coherency COH_Ci corresponding to each regiondominant color are obtained, and the coherency value COH_Ci and thecount value COUNT_PELS_Ci of the color pixels are multiplied, and theconfidence is added to the thusly multiplied value for thereby obtaininga confidence CM with respect to the region dominant color;

d) The confidence value is divided into region sizes SIZE_R for therebyobtaining a confidence with respect to the image region; and

e) The thusly obtained confidence is outputted.

Here, the region size SIZE_R is a size in the region and is computed bythe counting of the pixels in the region R.

At this time, there are two methods for computing the coherency COH_Ciwith respect to one dominant color Ci value.

A first method includes:

a step (1) for inputting a size of a coherency checking mask having acertain width and height, a step (2) for setting a count (COUNT_PELS_Ci)of the color pixels and a coherent total (TOTAL_NUM_COHERENT) to aninitial value(=0), and a step (3) which includes a step (3-1) forobtaining a count value (COUNT_PELS_Ci_PELS_Ci+1) of the color pixels byincreasing the color pixels with respect to all pixels PELj in theregion R which satisfies that the color of the pixel PELj is mapped tothe dominant color, a step (3-2) for obtaining the number of coherent(0˜WIDTW*HEIGHT)−1 by counting the number(except for the central pixels)of the is masked pixels in the case that the color pixels masked by thecentral arrangement of the coherence checking mask CCM are mapped to thedominant colors, and a step (3-3) for obtaining the total number of thecoherency (TOTAL_NUM_COHERENT) by summing the number of the coherencyand the total number of the coherency, a step (4) for obtaining acoherency value (COH_Ci) with respect to one dominant color value bydividing the total number of the obtained coherent values by a valueobtained by multiplying the total pixels(WIDTH*HEIGHT−1) to the totalnumber of coherences except for the count value of the pixel, and astep(5) for outputting a coherency value with respect to one dominantcolor value and the count values of the colors and the center pixels ofthe pixel colors.

The second method uses a threshold value and includes a step (1) forinputting a size of a coherency checking mask (CCM) having a certainwidth and height, a step (2) for setting a certain number of thresholdvalues (for example, WIDTH*HEIGHT−1), a step (3) for setting the countvalues of the color pixels, the total number of the coherency and thecount value of the non-boundary pixels to an initial value (=0),respectively, a step (4) which includes a step (4-1) with respect to allpixels in the region which satisfies that the pixel color is mapped tothe dominant color for obtaining the count values of the color pixels byup-counting the color pixels one by one, a step (4-2) for obtaining thecoherent number (0˜WIDTH×HEIGHT)−1 by counting the number(except for thecentral pixels) of the masked pixels in the case that the color pixelsmasked by the central arrangement of the coherency checking mask CCM aremapped to the dominant color, and a step (4-3) for obtaining a countvalue of the non-boundary pixels (NONBOUND_PELS) by increasing thenon-boundary pixels one by one in the case that the coherent number isthe same as or is larger than the boundary threshold value, a step (5)for obtaining a coherency value with respect to one dominant color bydividing the count value of the thusly obtained non-boundary pixels bythe count value of the color pixels, and a step (6) for outputting thecount values of the coherency value and the color pixels with respect toone dominant color.

In the above-described methods, as a condition for determining the colorwhich is mapped with the dominant color, when a difference between thedominant colors which are not clearly separated from other colors andthe pixel colors is smaller than NADCA, namely, DISTANCE(Ci,COLOR_OF_PELj)<NADCA, the above-described condition may be changed tothe above-described satisfying condition (step (1) of method 1, and step(4) of method 2).

In addition, as a condition for using the same color as the dominantcolor, when a difference between the dominant color and the masked pixelcolor is smaller than NADCA, namely, DISTANCE (Ci,COLOR_OF_MASKED_PEXELk)<NADCA, the condition may be changed to theabove-described condition(step (3-2) of the method 1, and step (4-2) ofthe method 2).

As the confidence is obtained with respect to the region dominant colorby the above-described method, it is possible to obtain aninteroperability during a search with respect to the region dominantcolor for a different feature extraction method using the confidencemeasure.

Namely, a certain region dominant color is obtained based on the regionto average value, and a certain region dominant color is obtained basedon a histogram. In this case, since there is a certain confidence value,the confidence value may be usefully used for a comparison of the regiondominant color values based on the different feature extractions.

In addition, the DCD extraction method shaping data is implemented asfollows:

DCD1=[N=1,{(C0=gray, P0=n/a)}, CM=0.01],

DCD2=[N=1,{(C0=gray, P0=n/a)}, CM=0.99],

DCD3=[N=2,{(C0=red, P0=50%)}, (C1=cyan, P1=50%)}, CM=0.99],

DCD4=[N=2,{(C0=red, P0=50%)}, (C1=cyan, P1=50%)}, CM=0.01],

DCD5=[N=n,{(C0=red, P0=10%)}, (C1=yellow, P1=5%), . . . , (Cn−1=gray,Pn−1=1%, CM=0.99]; The average color obtained based on DCD5 is assumedas “gray”.

DCD6=[N=n,{(C0=red, P0=10%)}, (C1=yellow, P1=5%), (Cn−1=gray, Pn−1=50%,CM=0.99]; The average color obtained based on DCD6 is assumed as “gray”.

The dominant colors of DCD2&DCD4&DCD6 are similar based on the regiondominant color descriptor, and the dominant colors of DCD1&DCD3&DCD5 aresimilar.

At this time, in the region dominant color descriptor, C0 is obtainedbased on an average value in DCD1, and (C0,P0),(C1,P1) of DCD3 arehistogram when the histogram is recognized, so that it is possible toobtain an average color C* and compare with the obtained average colorC* and C0 based on (C0,P0),(C1,P1).

In addition, it is possible to obtain a confidence CMi based on eachcolor Ci except for the confidence value with respect to the totalregion dominant color descriptor DCD.

Namely, DCD=[N,{Ci, Pi, CMi|0<i≦N)}, CM].

The confidence with respect to each color is determined based on variouselements as arranged in the confidence for the region, namely,normalized coherency (spatial variance), color mapping error CME (colorvariance), the size of region that the dominant color covers, and theposition of each color pixels in the region for thereby obtaining aconfidence value with respect to the determined color. Therefore, CMican be represented by a vector such that CMi=[SpatialVariance,ColorVariance, SizeOfCovers, Position].

The SpatialVariance which is inverse proportional to the coherency canbe defined similarly as in p24-26.

The ColorVariance value with respect to a certain color based on thecolor variance which is inverse proportional to the color mapping errorCME may be obtained based on the following equation.ColorVariance_Ci=SUM[Distance(CENTROID_Ci,MAPPING_COLOR_POINT_Pj_TO_Ci)/MAX_DISTANCE_Pj_TO_CI×NUM_MAPPING_COLOR_POINT_Pj_TO_Ci] for all jCM=Sum(CM_Ci) for all i/MAX_i+1

Namely, the ColorVariance with respect to a certain dominant color is adifference with respect to all colors which are recognized as a dominantcolor, and DISTANCE(CENTROID_Ci, MAPPING_COLOR_POINT_Pj_TO_Ci) is adifference with respect to the color Pj when the color is assumed as thedominant color Ci.

MAX_DISTANCE_Pj_TO_Ci is a maximum distance difference between twocolors (Pj, Ci).

NUM_MAPPING_COLOR_POINT_Pj_TO_Ci is the total number that the color Pjis mapped to Ci as the maximum value of j.

The values obtained by the above-described methods are normalized, andthe to confidence with respect to a certain dominant color has a valuebetween 0 and 1.

The confidences with respect to all dominant color values aresummed(SUM(CM_Ci) for all i(where i represents an integer, o<i<M) and isdivided by the maximum value(MAX_i+1) for thereby obtaining an averagevalue of CM_Ci, namely, the confidence CM with respect to the imageregion.

As described above, the region dominant color descriptor and theconfidence of the same are expressed based on a similarity of the colorwith respect to the image region, an error of the same, the size thatthe dominant color covers the region, and the position of the region, sothat it is possible to enhance a search performance and to provide aninteroperability between the region dominant colors based on differentextraction methods.

In addition, a standardized method is implemented by the extractionmethod description of the region dominant color descriptor using a colorspace descriptor, a quantization descriptor, a color cluster descriptor,and the number of color channels, so that it is possible to conduct acomparison search between the extracted region dominant colors extractedby various methods.

The present teaching can be readily applied to other types of apparatus.The description of the present invention is intended to be illustrative,and not to limit the scope of the claims. Many alternatives,modifications, and variations will be apparent to those skilled in theart.

1. A method for searching multimedia data in a multimedia device using aregion dominant color descriptor, the method comprising: extracting aregion dominant color descriptor from query multimedia data, the regiondominant color descriptor including a coherent degree of pixelscorresponding to a dominant color; comparing the extracted regiondominant color descriptor with at least one stored region dominant colordescriptor; searching multimedia data based on the coherent degree;obtaining an accuracy of the searched multimedia data; and outputtingthe accuracy of the searched multimedia data, wherein the regiondominant color descriptor further includes an accuracy of the regiondominant color indicating a degree of confidence of the region dominantcolor descriptor.
 2. The method of claim 1, wherein the region dominantcolor descriptor further includes a region dominant color, a numberquantifying region dominant colors, and a frequency of the regiondominant color.
 3. The method of claim 2, wherein the frequency of theregion dominant color is determined based on pixels corresponding to adominant color.
 4. The method of claim 1, further comprising: storingthe region dominant color descriptor.
 5. The method of claim 1, furthercomprising: extracting the region dominant color descriptor based on atleast one of an average-color method or a histogram method.